

import logging
import os
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from tempfile import TemporaryDirectory
from typing import List, Optional

import faiss
import torch
from datasets import Features, Sequence, Value, load_dataset

from transformers import DPRContextEncoder, DPRContextEncoderTokenizerFast, HfArgumentParser


lowerCAmelCase__						 =			logging.getLogger(__name__)
torch.set_grad_enabled(False)
lowerCAmelCase__						 =			"""cuda""" if torch.cuda.is_available() else """cpu"""



def        lowerCAmelCase__				(						SCREAMING_SNAKE_CASE_:				str						,							SCREAMING_SNAKE_CASE_:				Tuple=1_0_0						,							SCREAMING_SNAKE_CASE_:				str=" "   )      ->   List[str]:




				'''simple docstring'''





				A__          =      text.split(UpperCAmelCase_   )
				return [character.join(text[i : i + n]   ).strip() for i in range(0						,							len(UpperCAmelCase_   )						,							UpperCAmelCase_   )]



def        lowerCAmelCase__				(						SCREAMING_SNAKE_CASE_:				int   )      ->   dict:




				'''simple docstring'''





				A__     ,					A__          =      [], []
				for title, text in zip(documents["title"]						,							documents["text"]   ):
								if text is not None:
												for passage in split_text(UpperCAmelCase_   ):
																titles.append(title if title is not None else ""   )
																texts.append(UpperCAmelCase_   )
				return {"title": titles, "text": texts}



def        lowerCAmelCase__				(						SCREAMING_SNAKE_CASE_:				Any						,							SCREAMING_SNAKE_CASE_:				Union[str, Any]						,							SCREAMING_SNAKE_CASE_:				Dict   )      ->   dict:




				'''simple docstring'''





				A__          =      ctx_tokenizer(
				    documents["title"]						,							documents["text"]						,							truncation=UpperCAmelCase_						,							padding="longest"						,							return_tensors="pt"   )["input_ids"]
				A__          =      ctx_encoder(input_ids.to(device=UpperCAmelCase_   )						,							return_dict=UpperCAmelCase_   ).pooler_output
				return {"embeddings": embeddings.detach().cpu().numpy()}



def        lowerCAmelCase__				(						SCREAMING_SNAKE_CASE_:				int						,							SCREAMING_SNAKE_CASE_:				Optional[int]						,							SCREAMING_SNAKE_CASE_:				List[str]						,							)      ->   List[str]:




				'''simple docstring'''





				logger.info("Step 1 - Create the dataset"   )
				######################################

				# The dataset needed for RAG must have three columns:
				# - title (string): title of the document
				# - text (string): text of a passage of the document
				# - embeddings (array of dimension d): DPR representation of the passage
				# Let's say you have documents in tab-separated csv files with columns "title" and "text"
				assert os.path.isfile(rag_example_args.csv_path   ), "Please provide a valid path to a csv file"

				# You can load a Dataset object this way
				A__          =      load_dataset(
				    "csv"						,							data_files=[rag_example_args.csv_path]						,							split="train"						,							delimiter="\t"						,							column_names=["title", "text"]   )

				# More info about loading csv files in the documentation: https://huggingface.co/docs/datasets/loading_datasets.html?highlight=csv#csv-files

				# Then split the documents into passages of 100 words
				A__          =      dataset.map(UpperCAmelCase_						,							batched=UpperCAmelCase_						,							num_proc=processing_args.num_proc   )

				# And compute the embeddings
				A__          =      DPRContextEncoder.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name   ).to(device=UpperCAmelCase_   )
				A__          =      DPRContextEncoderTokenizerFast.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name   )
				A__          =      Features(
				    {"text": Value("string"   ), "title": Value("string"   ), "embeddings": Sequence(Value("float32"   )   )}   )  # optional, save as float32 instead of float64 to save space
				A__          =      dataset.map(
				    partial(UpperCAmelCase_						,							ctx_encoder=UpperCAmelCase_						,							ctx_tokenizer=UpperCAmelCase_   )						,							batched=UpperCAmelCase_						,							batch_size=processing_args.batch_size						,							features=UpperCAmelCase_						,							)

				# And finally save your dataset
				A__          =      os.path.join(rag_example_args.output_dir						,							"my_knowledge_dataset"   )
				dataset.save_to_disk(UpperCAmelCase_   )
				# from datasets import load_from_disk
				# dataset = load_from_disk(passages_path)  # to reload the dataset

				######################################
				logger.info("Step 2 - Index the dataset"   )
				######################################

				# Let's use the Faiss implementation of HNSW for fast approximate nearest neighbor search
				A__          =      faiss.IndexHNSWFlat(index_hnsw_args.d						,							index_hnsw_args.m						,							faiss.METRIC_INNER_PRODUCT   )
				dataset.add_faiss_index("embeddings"						,							custom_index=UpperCAmelCase_   )

				# And save the index
				A__          =      os.path.join(rag_example_args.output_dir						,							"my_knowledge_dataset_hnsw_index.faiss"   )
				dataset.get_index("embeddings"   ).save(UpperCAmelCase_   )
				# dataset.load_faiss_index("embeddings", index_path)  # to reload the index

@dataclass
class        a__    :
			"""simple docstring"""







			__lowerCamelCase										=							field(
			    default=str(Path(_lowerCAmelCase      ).parent / 'test_run' / 'dummy-kb' / 'my_knowledge_dataset.csv'      )				,						metadata={'help': 'Path to a tab-separated csv file with columns \'title\' and \'text\''}				,						)
			__lowerCamelCase										=							field(
			    default=_lowerCAmelCase				,						metadata={'help': 'Question that is passed as input to RAG. Default is \'What does Moses\' rod turn into ?\'.'}				,						)
			__lowerCamelCase										=							field(
			    default='facebook/rag-sequence-nq'				,						metadata={'help': 'The RAG model to use. Either \'facebook/rag-sequence-nq\' or \'facebook/rag-token-nq\''}				,						)
			__lowerCamelCase										=							field(
			    default='facebook/dpr-ctx_encoder-multiset-base'				,						metadata={
			        'help': (
			            'The DPR context encoder model to use. Either \'facebook/dpr-ctx_encoder-single-nq-base\' or'
			            ' \'facebook/dpr-ctx_encoder-multiset-base\''
			        )
			    }				,						)
			__lowerCamelCase										=							field(
			    default=str(Path(_lowerCAmelCase      ).parent / 'test_run' / 'dummy-kb'      )				,						metadata={'help': 'Path to a directory where the dataset passages and the index will be saved'}				,						)

@dataclass
class        a__    :
			"""simple docstring"""







			__lowerCamelCase										=							field(
			    default=_lowerCAmelCase				,						metadata={
			        'help': 'The number of processes to use to split the documents into passages. Default is single process.'
			    }				,						)
			__lowerCamelCase										=							field(
			    default=16				,						metadata={
			        'help': 'The batch size to use when computing the passages embeddings using the DPR context encoder.'
			    }				,						)

@dataclass
class        a__    :
			"""simple docstring"""







			__lowerCamelCase										=							field(
			    default=768				,						metadata={'help': 'The dimension of the embeddings to pass to the HNSW Faiss index.'}				,						)
			__lowerCamelCase										=							field(
			    default=128				,						metadata={
			        'help': (
			            'The number of bi-directional links created for every new element during the HNSW index construction.'
			        )
			    }				,						)


if __name__ == "__main__":
	logging.basicConfig(level=logging.WARNING)
	logger.setLevel(logging.INFO)

	lowerCAmelCase__						 =			HfArgumentParser((RagExampleArguments, ProcessingArguments, IndexHnswArguments))
	lowerCAmelCase__ ,  lowerCAmelCase__ ,  lowerCAmelCase__						 =			parser.parse_args_into_dataclasses()
	with TemporaryDirectory() as tmp_dir:
		lowerCAmelCase__						 =			rag_example_args.output_dir or tmp_dir
		main(rag_example_args, processing_args, index_hnsw_args)



"""simple docstring"""


import logging
import os
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from tempfile import TemporaryDirectory
from typing import List, Optional

import faiss
import torch
from datasets import Features, Sequence, Value, load_dataset

from transformers import DPRContextEncoder, DPRContextEncoderTokenizerFast, HfArgumentParser


SCREAMING_SNAKE_CASE			  =  logging.getLogger(__name__)
torch.set_grad_enabled(False)
SCREAMING_SNAKE_CASE			  =  """cuda""" if torch.cuda.is_available() else """cpu"""



def 						lowerCamelCase__      (				UpperCAmelCase_		,   UpperCAmelCase_=1_00		,   UpperCAmelCase_=" "					)->				List[str]:







       """simple docstring"""
       UpperCamelCase       			=		text.split(UpperCAmelCase_					)
       return [character.join(text[i : i + n]					).strip() for i in range(0		,   len(UpperCAmelCase_					)		,   UpperCAmelCase_					)]



def 						lowerCamelCase__      (				UpperCAmelCase_					)->				dict:







       """simple docstring"""
       UpperCamelCase			,							UpperCamelCase       			=		[], []
       for title, text in zip(documents["title"]		,   documents["text"]					):
              if text is not None:
                     for passage in split_text(UpperCAmelCase_					):
                            titles.append(title if title is not None else ""					)
                            texts.append(UpperCAmelCase_					)
       return {"title": titles, "text": texts}



def 						lowerCamelCase__      (				UpperCAmelCase_		,   UpperCAmelCase_		,   UpperCAmelCase_					)->				dict:







       """simple docstring"""
       UpperCamelCase       			=		ctx_tokenizer(
           documents["title"]		,   documents["text"]		,   truncation=UpperCAmelCase_		,   padding="longest"		,   return_tensors="pt"					)["input_ids"]
       UpperCamelCase       			=		ctx_encoder(input_ids.to(device=UpperCAmelCase_					)		,   return_dict=UpperCAmelCase_					).pooler_output
       return {"embeddings": embeddings.detach().cpu().numpy()}



def 						lowerCamelCase__      (				UpperCAmelCase_		,   UpperCAmelCase_		,   UpperCAmelCase_		,   )->				List[str]:







       """simple docstring"""
       ######################################
       logger.info("Step 1 - Create the dataset"					)
       ######################################

       # The dataset needed for RAG must have three columns:
       # - title (string): title of the document
       # - text (string): text of a passage of the document
       # - embeddings (array of dimension d): DPR representation of the passage
       # Let's say you have documents in tab-separated csv files with columns "title" and "text"
       assert os.path.isfile(rag_example_args.csv_path					), "Please provide a valid path to a csv file"

       # You can load a Dataset object this way
       UpperCamelCase       			=		load_dataset(
           "csv"		,   data_files=[rag_example_args.csv_path]		,   split="train"		,   delimiter="\t"		,   column_names=["title", "text"]					)

       # More info about loading csv files in the documentation: https://huggingface.co/docs/datasets/loading_datasets.html?highlight=csv#csv-files

       # Then split the documents into passages of 100 words
       UpperCamelCase       			=		dataset.map(UpperCAmelCase_		,   batched=UpperCAmelCase_		,   num_proc=processing_args.num_proc					)

       # And compute the embeddings
       UpperCamelCase       			=		DPRContextEncoder.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name					).to(device=UpperCAmelCase_					)
       UpperCamelCase       			=		DPRContextEncoderTokenizerFast.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name					)
       UpperCamelCase       			=		Features(
           {"text": Value("string"					), "title": Value("string"					), "embeddings": Sequence(Value("float32"					)					)}					)  # optional, save as float32 instead of float64 to save space
       UpperCamelCase       			=		dataset.map(
           partial(UpperCAmelCase_		,   ctx_encoder=UpperCAmelCase_		,   ctx_tokenizer=UpperCAmelCase_					)		,   batched=UpperCAmelCase_		,   batch_size=processing_args.batch_size		,   features=UpperCAmelCase_		,   )

       # And finally save your dataset
       UpperCamelCase       			=		os.path.join(rag_example_args.output_dir		,   "my_knowledge_dataset"					)
       dataset.save_to_disk(UpperCAmelCase_					)
       # from datasets import load_from_disk
       # dataset = load_from_disk(passages_path)  # to reload the dataset

       ######################################
       logger.info("Step 2 - Index the dataset"					)
       ######################################

       # Let's use the Faiss implementation of HNSW for fast approximate nearest neighbor search
       UpperCamelCase       			=		faiss.IndexHNSWFlat(index_hnsw_args.d		,   index_hnsw_args.m		,   faiss.METRIC_INNER_PRODUCT					)
       dataset.add_faiss_index("embeddings"		,   custom_index=UpperCAmelCase_					)

       # And save the index
       UpperCamelCase       			=		os.path.join(rag_example_args.output_dir		,   "my_knowledge_dataset_hnsw_index.faiss"					)
       dataset.get_index("embeddings"					).save(UpperCAmelCase_					)
       # dataset.load_faiss_index("embeddings", index_path)  # to reload the index



@dataclass
class 	__a	:
       UpperCamelCase_		:       str				=      field(
           default=str(Path(_lowerCAmelCase						).parent / '''test_run''' / '''dummy-kb''' / '''my_knowledge_dataset.csv'''						)     ,  metadata={'''help''': '''Path to a tab-separated csv file with columns \'title\' and \'text\''''}     ,  )
       UpperCamelCase_		:       Optional[str]				=      field(
           default=_lowerCAmelCase     ,  metadata={'''help''': '''Question that is passed as input to RAG. Default is \'What does Moses\' rod turn into ?\'.'''}     ,  )
       UpperCamelCase_		:       str				=      field(
           default='''facebook/rag-sequence-nq'''     ,  metadata={'''help''': '''The RAG model to use. Either \'facebook/rag-sequence-nq\' or \'facebook/rag-token-nq\''''}     ,  )
       UpperCamelCase_		:       str				=      field(
           default='''facebook/dpr-ctx_encoder-multiset-base'''     ,  metadata={
               '''help''': (
                   '''The DPR context encoder model to use. Either \'facebook/dpr-ctx_encoder-single-nq-base\' or'''
                   ''' \'facebook/dpr-ctx_encoder-multiset-base\''''
               )
           }     ,  )
       UpperCamelCase_		:       Optional[str]				=      field(
           default=str(Path(_lowerCAmelCase						).parent / '''test_run''' / '''dummy-kb'''						)     ,  metadata={'''help''': '''Path to a directory where the dataset passages and the index will be saved'''}     ,  )





@dataclass
class 	__a	:
       UpperCamelCase_		:       Optional[int]				=      field(
           default=_lowerCAmelCase     ,  metadata={
               '''help''': '''The number of processes to use to split the documents into passages. Default is single process.'''
           }     ,  )
       UpperCamelCase_		:       int				=      field(
           default=16     ,  metadata={
               '''help''': '''The batch size to use when computing the passages embeddings using the DPR context encoder.'''
           }     ,  )





@dataclass
class 	__a	:
       UpperCamelCase_		:       int				=      field(
           default=768     ,  metadata={'''help''': '''The dimension of the embeddings to pass to the HNSW Faiss index.'''}     ,  )
       UpperCamelCase_		:       int				=      field(
           default=128     ,  metadata={
               '''help''': (
                   '''The number of bi-directional links created for every new element during the HNSW index construction.'''
               )
           }     ,  )


if __name__ == "__main__":
      logging.basicConfig(level=logging.WARNING)
      logger.setLevel(logging.INFO)

      SCREAMING_SNAKE_CASE			  =  HfArgumentParser((RagExampleArguments, ProcessingArguments, IndexHnswArguments))
      SCREAMING_SNAKE_CASE  ,   SCREAMING_SNAKE_CASE  ,   SCREAMING_SNAKE_CASE			  =  parser.parse_args_into_dataclasses()
      with TemporaryDirectory() as tmp_dir:
            SCREAMING_SNAKE_CASE			  =  rag_example_args.output_dir or tmp_dir
            main(rag_example_args, processing_args, index_hnsw_args)





